Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations340
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory255.6 KiB
Average record size in memory769.9 B

Variable types

Text5
Categorical4
Numeric18

Alerts

Carbohydrates is highly overall correlated with Energy and 1 other fieldsHigh correlation
Category is highly overall correlated with Country and 1 other fieldsHigh correlation
Cholestrol is highly overall correlated with Dietary_Fibre and 5 other fieldsHigh correlation
Country is highly overall correlated with Category and 8 other fieldsHigh correlation
Customers is highly overall correlated with Gross_Profit_Margin and 1 other fieldsHigh correlation
Dietary_Fibre is highly overall correlated with Cholestrol and 5 other fieldsHigh correlation
Energy is highly overall correlated with Carbohydrates and 6 other fieldsHigh correlation
Gross_Profit_Margin is highly overall correlated with Country and 1 other fieldsHigh correlation
Latitude is highly overall correlated with Country and 5 other fieldsHigh correlation
Longitude is highly overall correlated with Country and 5 other fieldsHigh correlation
Number_of_Employees is highly overall correlated with Country and 2 other fieldsHigh correlation
Ownership_Type is highly overall correlated with Country and 5 other fieldsHigh correlation
Profits is highly overall correlated with Country and 6 other fieldsHigh correlation
Protein is highly overall correlated with Cholestrol and 5 other fieldsHigh correlation
Revenue is highly overall correlated with Country and 5 other fieldsHigh correlation
Saturated_Fat is highly overall correlated with Cholestrol and 6 other fieldsHigh correlation
Serve_Size is highly overall correlated with SugarsHigh correlation
Sodium is highly overall correlated with Cholestrol and 5 other fieldsHigh correlation
State is highly overall correlated with Category and 4 other fieldsHigh correlation
Sugars is highly overall correlated with Carbohydrates and 1 other fieldsHigh correlation
Total_Fat is highly overall correlated with Cholestrol and 5 other fieldsHigh correlation
Trans_Fat is highly overall correlated with Saturated_FatHigh correlation
Store_ID has unique values Unique
Store_Name has unique values Unique
Profits has unique values Unique
Gross_Profit_Margin has unique values Unique
Best_Selling_Item has unique values Unique
Energy has 18 (5.3%) zeros Zeros
Protein has 50 (14.7%) zeros Zeros
Total_Fat has 71 (20.9%) zeros Zeros
Saturated_Fat has 83 (24.4%) zeros Zeros
Trans_Fat has 229 (67.4%) zeros Zeros
Cholestrol has 70 (20.6%) zeros Zeros
Carbohydrates has 19 (5.6%) zeros Zeros
Sugars has 36 (10.6%) zeros Zeros
Dietary_Fibre has 117 (34.4%) zeros Zeros
Sodium has 14 (4.1%) zeros Zeros

Reproduction

Analysis started2025-05-22 15:10:05.861997
Analysis finished2025-05-22 15:10:59.347558
Duration53.49 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Store_ID
Text

Unique 

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
2025-05-22T20:40:59.668278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.385294
Min length7

Characters and Unicode

Total characters3871
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)100.0%

Sample

1st row23149-228271
2nd row23191-228548
3rd row23193-228546
4th row23180-228545
5th row24457-238129
ValueCountFrequency (%)
23149-228271 1
 
0.3%
25522-241268 1
 
0.3%
23193-228546 1
 
0.3%
23180-228545 1
 
0.3%
24457-238129 1
 
0.3%
23664-232349 1
 
0.3%
20874-208485 1
 
0.3%
29491-254065 1
 
0.3%
22446-221249 1
 
0.3%
23392-229965 1
 
0.3%
Other values (330) 330
97.1%
2025-05-22T20:41:00.234573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 571
14.8%
1 452
11.7%
7 353
9.1%
4 342
8.8%
- 340
8.8%
0 329
8.5%
6 312
8.1%
9 311
8.0%
3 298
7.7%
5 293
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3531
91.2%
Dash Punctuation 340
 
8.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 571
16.2%
1 452
12.8%
7 353
10.0%
4 342
9.7%
0 329
9.3%
6 312
8.8%
9 311
8.8%
3 298
8.4%
5 293
8.3%
8 270
7.6%
Dash Punctuation
ValueCountFrequency (%)
- 340
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3871
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 571
14.8%
1 452
11.7%
7 353
9.1%
4 342
8.8%
- 340
8.8%
0 329
8.5%
6 312
8.1%
9 311
8.0%
3 298
7.7%
5 293
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3871
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 571
14.8%
1 452
11.7%
7 353
9.1%
4 342
8.8%
- 340
8.8%
0 329
8.5%
6 312
8.1%
9 311
8.0%
3 298
7.7%
5 293
7.6%

Store_Name
Text

Unique 

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.9 KiB
2025-05-22T20:41:00.788920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length25
Mean length20.708824
Min length4

Characters and Unicode

Total characters7041
Distinct characters72
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)100.0%

Sample

1st rowBanjara Hills
2nd rowKukatpally
3rd rowMadhapur
4th rowJubilee Hills
5th rowHi-Tech City
ValueCountFrequency (%)
152
 
12.7%
target 30
 
2.5%
west 15
 
1.3%
hwy 12
 
1.0%
ave 12
 
1.0%
mall 12
 
1.0%
kroger 11
 
0.9%
st 10
 
0.8%
city 10
 
0.8%
safeway 9
 
0.8%
Other values (656) 926
77.2%
2025-05-22T20:41:01.591632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
859
 
12.2%
a 604
 
8.6%
e 521
 
7.4%
t 357
 
5.1%
r 355
 
5.0%
n 343
 
4.9%
l 308
 
4.4%
o 291
 
4.1%
i 284
 
4.0%
s 181
 
2.6%
Other values (62) 2938
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4358
61.9%
Uppercase Letter 1014
 
14.4%
Space Separator 859
 
12.2%
Decimal Number 483
 
6.9%
Other Punctuation 164
 
2.3%
Dash Punctuation 159
 
2.3%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 604
13.9%
e 521
12.0%
t 357
 
8.2%
r 355
 
8.1%
n 343
 
7.9%
l 308
 
7.1%
o 291
 
6.7%
i 284
 
6.5%
s 181
 
4.2%
h 146
 
3.4%
Other values (16) 968
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 115
 
11.3%
C 107
 
10.6%
T 96
 
9.5%
A 72
 
7.1%
M 60
 
5.9%
H 57
 
5.6%
P 55
 
5.4%
B 48
 
4.7%
K 46
 
4.5%
F 44
 
4.3%
Other values (15) 314
31.0%
Decimal Number
ValueCountFrequency (%)
1 90
18.6%
2 71
14.7%
4 50
10.4%
0 50
10.4%
7 49
10.1%
3 39
8.1%
5 39
8.1%
6 34
 
7.0%
8 32
 
6.6%
9 29
 
6.0%
Other Punctuation
ValueCountFrequency (%)
& 70
42.7%
# 58
35.4%
. 15
 
9.1%
, 12
 
7.3%
' 5
 
3.0%
@ 3
 
1.8%
/ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
859
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 159
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5372
76.3%
Common 1669
 
23.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 604
 
11.2%
e 521
 
9.7%
t 357
 
6.6%
r 355
 
6.6%
n 343
 
6.4%
l 308
 
5.7%
o 291
 
5.4%
i 284
 
5.3%
s 181
 
3.4%
h 146
 
2.7%
Other values (41) 1982
36.9%
Common
ValueCountFrequency (%)
859
51.5%
- 159
 
9.5%
1 90
 
5.4%
2 71
 
4.3%
& 70
 
4.2%
# 58
 
3.5%
4 50
 
3.0%
0 50
 
3.0%
7 49
 
2.9%
3 39
 
2.3%
Other values (11) 174
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7041
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
859
 
12.2%
a 604
 
8.6%
e 521
 
7.4%
t 357
 
5.1%
r 355
 
5.0%
n 343
 
4.9%
l 308
 
4.4%
o 291
 
4.1%
i 284
 
4.0%
s 181
 
2.6%
Other values (62) 2938
41.7%

Ownership_Type
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
Company Owned
137 
Licensed
121 
Joint Venture
82 

Length

Max length13
Median length13
Mean length11.220588
Min length8

Characters and Unicode

Total characters3815
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJoint Venture
2nd rowJoint Venture
3rd rowJoint Venture
4th rowJoint Venture
5th rowJoint Venture

Common Values

ValueCountFrequency (%)
Company Owned 137
40.3%
Licensed 121
35.6%
Joint Venture 82
24.1%

Length

2025-05-22T20:41:01.765529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T20:41:01.975150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
company 137
24.5%
owned 137
24.5%
licensed 121
21.6%
joint 82
14.7%
venture 82
14.7%

Most occurring characters

ValueCountFrequency (%)
n 559
14.7%
e 543
14.2%
d 258
 
6.8%
o 219
 
5.7%
219
 
5.7%
i 203
 
5.3%
t 164
 
4.3%
w 137
 
3.6%
C 137
 
3.6%
O 137
 
3.6%
Other values (11) 1239
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3037
79.6%
Uppercase Letter 559
 
14.7%
Space Separator 219
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 559
18.4%
e 543
17.9%
d 258
8.5%
o 219
 
7.2%
i 203
 
6.7%
t 164
 
5.4%
w 137
 
4.5%
y 137
 
4.5%
a 137
 
4.5%
p 137
 
4.5%
Other values (5) 543
17.9%
Uppercase Letter
ValueCountFrequency (%)
C 137
24.5%
O 137
24.5%
L 121
21.6%
J 82
14.7%
V 82
14.7%
Space Separator
ValueCountFrequency (%)
219
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3596
94.3%
Common 219
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 559
15.5%
e 543
15.1%
d 258
 
7.2%
o 219
 
6.1%
i 203
 
5.6%
t 164
 
4.6%
w 137
 
3.8%
C 137
 
3.8%
O 137
 
3.8%
y 137
 
3.8%
Other values (10) 1102
30.6%
Common
ValueCountFrequency (%)
219
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3815
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 559
14.7%
e 543
14.2%
d 258
 
6.8%
o 219
 
5.7%
219
 
5.7%
i 203
 
5.3%
t 164
 
4.3%
w 137
 
3.6%
C 137
 
3.6%
O 137
 
3.6%
Other values (11) 1239
32.5%
Distinct339
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size28.9 KiB
2025-05-22T20:41:02.491135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length81
Median length66
Mean length29.508824
Min length8

Characters and Unicode

Total characters10033
Distinct characters70
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique338 ?
Unique (%)99.4%

Sample

1st rowLower Ground Floor, GVK One, Road Number 1, Banjara Hills
2nd rowUpper Ground Floor, Forum Sujana Mall, Kukatpally
3rd rowLower Ground Floor, Inorbit Mall, Madhapur
4th rowGround Floor, Road No. 92, Near Apollo hospital, Jubilee Hills
5th rowUpper Ground Floor, Phoenix tower A, Opposite Trident Hotel, Madhapur Village
ValueCountFrequency (%)
floor 51
 
2.9%
ground 45
 
2.6%
road 40
 
2.3%
street 33
 
1.9%
st 27
 
1.5%
rd 26
 
1.5%
e 26
 
1.5%
ave 25
 
1.4%
avenue 22
 
1.3%
west 22
 
1.3%
Other values (864) 1435
81.9%
2025-05-22T20:41:03.364474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1421
 
14.2%
a 658
 
6.6%
e 617
 
6.1%
r 547
 
5.5%
o 458
 
4.6%
t 455
 
4.5%
n 419
 
4.2%
l 383
 
3.8%
i 343
 
3.4%
, 278
 
2.8%
Other values (60) 4454
44.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5543
55.2%
Uppercase Letter 1498
 
14.9%
Space Separator 1421
 
14.2%
Decimal Number 1182
 
11.8%
Other Punctuation 353
 
3.5%
Close Punctuation 14
 
0.1%
Open Punctuation 14
 
0.1%
Dash Punctuation 8
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 658
11.9%
e 617
11.1%
r 547
9.9%
o 458
 
8.3%
t 455
 
8.2%
n 419
 
7.6%
l 383
 
6.9%
i 343
 
6.2%
d 254
 
4.6%
u 210
 
3.8%
Other values (16) 1199
21.6%
Uppercase Letter
ValueCountFrequency (%)
S 180
 
12.0%
A 117
 
7.8%
C 98
 
6.5%
M 97
 
6.5%
F 91
 
6.1%
G 90
 
6.0%
R 88
 
5.9%
B 87
 
5.8%
P 87
 
5.8%
W 76
 
5.1%
Other values (14) 487
32.5%
Decimal Number
ValueCountFrequency (%)
1 244
20.6%
0 210
17.8%
2 147
12.4%
5 117
9.9%
3 109
9.2%
4 95
 
8.0%
7 68
 
5.8%
8 65
 
5.5%
6 64
 
5.4%
9 63
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 278
78.8%
. 55
 
15.6%
# 10
 
2.8%
/ 4
 
1.1%
& 4
 
1.1%
' 2
 
0.6%
Space Separator
ValueCountFrequency (%)
1421
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7041
70.2%
Common 2992
29.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 658
 
9.3%
e 617
 
8.8%
r 547
 
7.8%
o 458
 
6.5%
t 455
 
6.5%
n 419
 
6.0%
l 383
 
5.4%
i 343
 
4.9%
d 254
 
3.6%
u 210
 
3.0%
Other values (40) 2697
38.3%
Common
ValueCountFrequency (%)
1421
47.5%
, 278
 
9.3%
1 244
 
8.2%
0 210
 
7.0%
2 147
 
4.9%
5 117
 
3.9%
3 109
 
3.6%
4 95
 
3.2%
7 68
 
2.3%
8 65
 
2.2%
Other values (10) 238
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1421
 
14.2%
a 658
 
6.6%
e 617
 
6.1%
r 547
 
5.5%
o 458
 
4.6%
t 455
 
4.5%
n 419
 
4.2%
l 383
 
3.8%
i 343
 
3.4%
, 278
 
2.8%
Other values (60) 4454
44.4%

City
Text

Distinct139
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Memory size21.9 KiB
2025-05-22T20:41:03.674570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length15
Mean length8.5294118
Min length4

Characters and Unicode

Total characters2900
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)20.6%

Sample

1st rowHyderabad
2nd rowHyderabad
3rd rowHyderabad
4th rowHyderabad
5th rowHyderabad
ValueCountFrequency (%)
mumbai 29
 
6.8%
new 19
 
4.5%
delhi 16
 
3.8%
city 11
 
2.6%
miami 10
 
2.3%
san 10
 
2.3%
washington 10
 
2.3%
bangalore 10
 
2.3%
beach 9
 
2.1%
pune 8
 
1.9%
Other values (138) 294
69.0%
2025-05-22T20:41:04.120651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 304
 
10.5%
e 239
 
8.2%
i 225
 
7.8%
n 218
 
7.5%
l 199
 
6.9%
o 189
 
6.5%
t 122
 
4.2%
r 122
 
4.2%
u 103
 
3.6%
s 101
 
3.5%
Other values (40) 1078
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2366
81.6%
Uppercase Letter 446
 
15.4%
Space Separator 86
 
3.0%
Other Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 304
12.8%
e 239
10.1%
i 225
9.5%
n 218
9.2%
l 199
 
8.4%
o 189
 
8.0%
t 122
 
5.2%
r 122
 
5.2%
u 103
 
4.4%
s 101
 
4.3%
Other values (14) 544
23.0%
Uppercase Letter
ValueCountFrequency (%)
C 69
15.5%
M 53
11.9%
B 45
10.1%
D 40
9.0%
H 31
 
7.0%
A 25
 
5.6%
N 24
 
5.4%
S 23
 
5.2%
P 20
 
4.5%
F 19
 
4.3%
Other values (13) 97
21.7%
Other Punctuation
ValueCountFrequency (%)
' 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2812
97.0%
Common 88
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 304
 
10.8%
e 239
 
8.5%
i 225
 
8.0%
n 218
 
7.8%
l 199
 
7.1%
o 189
 
6.7%
t 122
 
4.3%
r 122
 
4.3%
u 103
 
3.7%
s 101
 
3.6%
Other values (37) 990
35.2%
Common
ValueCountFrequency (%)
86
97.7%
' 1
 
1.1%
, 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 304
 
10.5%
e 239
 
8.2%
i 225
 
7.8%
n 218
 
7.5%
l 199
 
6.9%
o 189
 
6.5%
t 122
 
4.2%
r 122
 
4.2%
u 103
 
3.6%
s 101
 
3.5%
Other values (40) 1078
37.2%

State
Categorical

High correlation 

Distinct32
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
MH
38 
CA
 
20
DL
 
17
AR
 
15
TN
 
15
Other values (27)
235 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters680
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowAP
2nd rowAP
3rd rowAP
4th rowAP
5th rowAP

Common Values

ValueCountFrequency (%)
MH 38
 
11.2%
CA 20
 
5.9%
DL 17
 
5.0%
AR 15
 
4.4%
TN 15
 
4.4%
IN 10
 
2.9%
ID 10
 
2.9%
LA 10
 
2.9%
KS 10
 
2.9%
NY 10
 
2.9%
Other values (22) 185
54.4%

Length

2025-05-22T20:41:04.339725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mh 38
 
11.2%
ca 20
 
5.9%
dl 17
 
5.0%
ar 15
 
4.4%
tn 15
 
4.4%
il 10
 
2.9%
dc 10
 
2.9%
fl 10
 
2.9%
ut 10
 
2.9%
ia 10
 
2.9%
Other values (22) 185
54.4%

Most occurring characters

ValueCountFrequency (%)
A 120
17.6%
H 63
9.3%
L 57
 
8.4%
I 50
 
7.4%
M 43
 
6.3%
D 42
 
6.2%
C 40
 
5.9%
O 38
 
5.6%
K 38
 
5.6%
N 35
 
5.1%
Other values (11) 154
22.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 680
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 120
17.6%
H 63
9.3%
L 57
 
8.4%
I 50
 
7.4%
M 43
 
6.3%
D 42
 
6.2%
C 40
 
5.9%
O 38
 
5.6%
K 38
 
5.6%
N 35
 
5.1%
Other values (11) 154
22.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 680
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 120
17.6%
H 63
9.3%
L 57
 
8.4%
I 50
 
7.4%
M 43
 
6.3%
D 42
 
6.2%
C 40
 
5.9%
O 38
 
5.6%
K 38
 
5.6%
N 35
 
5.1%
Other values (11) 154
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 120
17.6%
H 63
9.3%
L 57
 
8.4%
I 50
 
7.4%
M 43
 
6.3%
D 42
 
6.2%
C 40
 
5.9%
O 38
 
5.6%
K 38
 
5.6%
N 35
 
5.1%
Other values (11) 154
22.6%

Country
Categorical

High correlation 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
US
258 
IN
82 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters680
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN
2nd rowIN
3rd rowIN
4th rowIN
5th rowIN

Common Values

ValueCountFrequency (%)
US 258
75.9%
IN 82
 
24.1%

Length

2025-05-22T20:41:04.512817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-22T20:41:04.653504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
us 258
75.9%
in 82
 
24.1%

Most occurring characters

ValueCountFrequency (%)
U 258
37.9%
S 258
37.9%
I 82
 
12.1%
N 82
 
12.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 680
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 258
37.9%
S 258
37.9%
I 82
 
12.1%
N 82
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 680
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 258
37.9%
S 258
37.9%
I 82
 
12.1%
N 82
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 680
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 258
37.9%
S 258
37.9%
I 82
 
12.1%
N 82
 
12.1%

Longitude
Real number (ℝ)

High correlation 

Distinct260
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-56.761
Minimum-158.02
Maximum80.26
Zeros0
Zeros (%)0.0%
Negative258
Negative (%)75.9%
Memory size2.8 KiB
2025-05-22T20:41:04.819679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-158.02
5-th percentile-149.144
Q1-99.325
median-87.265
Q3-71.1225
95-th percentile77.591
Maximum80.26
Range238.28
Interquartile range (IQR)28.2025

Descriptive statistics

Standard deviation76.967267
Coefficient of variation (CV)-1.3559886
Kurtosis-0.63566145
Mean-56.761
Median Absolute Deviation (MAD)16.055
Skewness1.0036993
Sum-19298.74
Variance5923.9601
MonotonicityNot monotonic
2025-05-22T20:41:05.036861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.83 8
 
2.4%
-112.02 4
 
1.2%
72.82 4
 
1.2%
-149.41 3
 
0.9%
77.1 3
 
0.9%
-77.05 3
 
0.9%
72.84 3
 
0.9%
-86.16 3
 
0.9%
-77.04 3
 
0.9%
-122.28 3
 
0.9%
Other values (250) 303
89.1%
ValueCountFrequency (%)
-158.02 2
0.6%
-157.95 1
0.3%
-157.86 1
0.3%
-157.84 1
0.3%
-157.83 1
0.3%
-155.08 1
0.3%
-155.07 2
0.6%
-155.06 1
0.3%
-151.07 1
0.3%
-151.05 1
0.3%
ValueCountFrequency (%)
80.26 1
0.3%
80.25 2
0.6%
80.22 1
0.3%
80.21 1
0.3%
78.45 1
0.3%
78.42 1
0.3%
78.39 2
0.6%
78.38 2
0.6%
77.73 1
0.3%
77.7 1
0.3%

Latitude
Real number (ℝ)

High correlation 

Distinct245
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.487647
Minimum12.91
Maximum61.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:05.255757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.91
5-th percentile17.4295
Q128.5225
median35.105
Q340.1
95-th percentile44.0405
Maximum61.6
Range48.69
Interquartile range (IQR)11.5775

Descriptive statistics

Standard deviation9.9451689
Coefficient of variation (CV)0.29698022
Kurtosis0.3266554
Mean33.487647
Median Absolute Deviation (MAD)5.565
Skewness-0.1162333
Sum11385.8
Variance98.906384
MonotonicityNot monotonic
2025-05-22T20:41:05.449401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.1 4
 
1.2%
29.95 4
 
1.2%
12.99 4
 
1.2%
39.13 4
 
1.2%
61.58 4
 
1.2%
38.9 4
 
1.2%
37.78 4
 
1.2%
28.57 3
 
0.9%
28.56 3
 
0.9%
34.11 3
 
0.9%
Other values (235) 303
89.1%
ValueCountFrequency (%)
12.91 1
 
0.3%
12.94 1
 
0.3%
12.97 2
0.6%
12.99 4
1.2%
13 1
 
0.3%
13.01 2
0.6%
13.03 1
 
0.3%
13.06 2
0.6%
13.09 1
 
0.3%
17.42 2
0.6%
ValueCountFrequency (%)
61.6 2
0.6%
61.58 4
1.2%
60.49 1
 
0.3%
60.48 1
 
0.3%
60.13 1
 
0.3%
47.74 1
 
0.3%
47.69 1
 
0.3%
45.42 1
 
0.3%
44.93 2
0.6%
44.59 1
 
0.3%

Revenue
Real number (ℝ)

High correlation 

Distinct339
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.853416
Minimum1.0010987
Maximum49.680624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:05.642235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.0010987
5-th percentile2.7203604
Q115.301218
median27.056185
Q337.930464
95-th percentile46.543474
Maximum49.680624
Range48.679525
Interquartile range (IQR)22.629246

Descriptive statistics

Standard deviation14.47698
Coefficient of variation (CV)0.55996392
Kurtosis-1.2053981
Mean25.853416
Median Absolute Deviation (MAD)11.147191
Skewness-0.16573861
Sum8790.1614
Variance209.58295
MonotonicityNot monotonic
2025-05-22T20:41:05.867417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.85900449 2
 
0.6%
2.117343669 1
 
0.3%
31.10446486 1
 
0.3%
26.10660115 1
 
0.3%
38.18628498 1
 
0.3%
31.00726341 1
 
0.3%
35.96453749 1
 
0.3%
40.08224738 1
 
0.3%
32.63298441 1
 
0.3%
28.80367443 1
 
0.3%
Other values (329) 329
96.8%
ValueCountFrequency (%)
1.001098666 1
0.3%
1.058503983 1
0.3%
1.096957305 1
0.3%
1.305429243 1
0.3%
1.776757103 1
0.3%
1.871242409 1
0.3%
1.896511734 1
0.3%
2.03521836 1
0.3%
2.093997009 1
0.3%
2.101413007 1
0.3%
ValueCountFrequency (%)
49.6806238 1
0.3%
49.41572314 1
0.3%
49.04400769 1
0.3%
48.90728477 1
0.3%
48.77910703 1
0.3%
48.7449263 1
0.3%
48.68938261 1
0.3%
48.56975005 1
0.3%
48.42448195 1
0.3%
48.29096347 1
0.3%

Profits
Real number (ℝ)

High correlation  Unique 

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6436062
Minimum0.050085485
Maximum13.514181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:06.058549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.050085485
5-th percentile0.2660243
Q11.8297619
median3.8930494
Q36.9198127
95-th percentile11.449931
Maximum13.514181
Range13.464096
Interquartile range (IQR)5.0900508

Descriptive statistics

Standard deviation3.406981
Coefficient of variation (CV)0.73369291
Kurtosis-0.50969402
Mean4.6436062
Median Absolute Deviation (MAD)2.3314482
Skewness0.67165784
Sum1578.8261
Variance11.607519
MonotonicityNot monotonic
2025-05-22T20:41:06.268159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1715838652 1
 
0.3%
3.675155253 1
 
0.3%
3.853287164 1
 
0.3%
7.609812097 1
 
0.3%
1.622518199 1
 
0.3%
7.220841013 1
 
0.3%
8.252169838 1
 
0.3%
4.35944683 1
 
0.3%
6.656218242 1
 
0.3%
7.838446593 1
 
0.3%
Other values (330) 330
97.1%
ValueCountFrequency (%)
0.05008548536 1
0.3%
0.05464538521 1
0.3%
0.05780225477 1
0.3%
0.07634691395 1
0.3%
0.127174208 1
0.3%
0.1388483911 1
0.3%
0.1420546152 1
0.3%
0.1602857906 1
0.3%
0.1683339189 1
0.3%
0.1693629731 1
0.3%
ValueCountFrequency (%)
13.5141811 1
0.3%
13.43148044 1
0.3%
12.74468553 1
0.3%
12.73350879 1
0.3%
12.53632059 1
0.3%
12.41756197 1
0.3%
12.32888718 1
0.3%
12.32536377 1
0.3%
12.14460164 1
0.3%
12.11876903 1
0.3%

Gross_Profit_Margin
Real number (ℝ)

High correlation  Unique 

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5233102
Minimum-4.8819014
Maximum19.459398
Zeros0
Zeros (%)0.0%
Negative82
Negative (%)24.1%
Memory size2.8 KiB
2025-05-22T20:41:06.436161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.8819014
5-th percentile-2.8731462
Q10.069328075
median1.4339934
Q36.1845798
95-th percentile14.155887
Maximum19.459398
Range24.3413
Interquartile range (IQR)6.1152517

Descriptive statistics

Standard deviation5.1930817
Coefficient of variation (CV)1.4739212
Kurtosis0.14986816
Mean3.5233102
Median Absolute Deviation (MAD)2.6481124
Skewness0.91877869
Sum1197.9255
Variance26.968097
MonotonicityNot monotonic
2025-05-22T20:41:06.593024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7477324177 1
 
0.3%
2.879842928 1
 
0.3%
5.396561996 1
 
0.3%
3.551696262 1
 
0.3%
12.90860566 1
 
0.3%
3.226732552 1
 
0.3%
3.146077824 1
 
0.3%
7.751317257 1
 
0.3%
0.6634154207 1
 
0.3%
5.814263285 1
 
0.3%
Other values (330) 330
97.1%
ValueCountFrequency (%)
-4.881901355 1
0.3%
-4.788908927 1
0.3%
-4.582111383 1
0.3%
-4.535137163 1
0.3%
-4.511879521 1
0.3%
-4.022706074 1
0.3%
-3.718753123 1
0.3%
-3.699139875 1
0.3%
-3.658840345 1
0.3%
-3.558528488 1
0.3%
ValueCountFrequency (%)
19.45939839 1
0.3%
18.17895448 1
0.3%
17.63908664 1
0.3%
17.45697703 1
0.3%
17.05612415 1
0.3%
16.99365265 1
0.3%
16.86371152 1
0.3%
16.56374384 1
0.3%
16.26982094 1
0.3%
15.65865426 1
0.3%

Number_of_Employees
Real number (ℝ)

High correlation 

Distinct109
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.067647
Minimum25
Maximum149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-22T20:41:06.768143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile38.95
Q164
median89
Q3115
95-th percentile140
Maximum149
Range124
Interquartile range (IQR)51

Descriptive statistics

Standard deviation31.378433
Coefficient of variation (CV)0.34838739
Kurtosis-0.95730398
Mean90.067647
Median Absolute Deviation (MAD)25
Skewness0.044021898
Sum30623
Variance984.60603
MonotonicityNot monotonic
2025-05-22T20:41:06.970898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 9
 
2.6%
70 8
 
2.4%
64 7
 
2.1%
59 7
 
2.1%
133 6
 
1.8%
75 6
 
1.8%
139 6
 
1.8%
135 6
 
1.8%
87 6
 
1.8%
90 6
 
1.8%
Other values (99) 273
80.3%
ValueCountFrequency (%)
25 3
0.9%
27 1
 
0.3%
31 1
 
0.3%
32 2
 
0.6%
33 1
 
0.3%
34 5
1.5%
35 2
 
0.6%
36 1
 
0.3%
38 1
 
0.3%
39 1
 
0.3%
ValueCountFrequency (%)
149 1
 
0.3%
148 1
 
0.3%
147 1
 
0.3%
146 4
1.2%
145 4
1.2%
144 1
 
0.3%
143 2
0.6%
142 1
 
0.3%
141 1
 
0.3%
140 2
0.6%

Customers
Real number (ℝ)

High correlation 

Distinct338
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13071.494
Minimum1002
Maximum24964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-22T20:41:07.131030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1002
5-th percentile2186.3
Q16877.25
median13373
Q319244.25
95-th percentile23603.6
Maximum24964
Range23962
Interquartile range (IQR)12367

Descriptive statistics

Standard deviation6993.9103
Coefficient of variation (CV)0.53505056
Kurtosis-1.2246175
Mean13071.494
Median Absolute Deviation (MAD)6127.5
Skewness-0.076018825
Sum4444308
Variance48914781
MonotonicityNot monotonic
2025-05-22T20:41:07.331238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9932 2
 
0.6%
4331 2
 
0.6%
3979 1
 
0.3%
13542 1
 
0.3%
10369 1
 
0.3%
15331 1
 
0.3%
1223 1
 
0.3%
15474 1
 
0.3%
15964 1
 
0.3%
9024 1
 
0.3%
Other values (328) 328
96.5%
ValueCountFrequency (%)
1002 1
0.3%
1119 1
0.3%
1128 1
0.3%
1156 1
0.3%
1223 1
0.3%
1258 1
0.3%
1347 1
0.3%
1442 1
0.3%
1528 1
0.3%
1627 1
0.3%
ValueCountFrequency (%)
24964 1
0.3%
24780 1
0.3%
24599 1
0.3%
24465 1
0.3%
24344 1
0.3%
24250 1
0.3%
24205 1
0.3%
24162 1
0.3%
24139 1
0.3%
24101 1
0.3%

Best_Selling_Item
Text

Unique 

Distinct340
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size29.2 KiB
2025-05-22T20:41:07.710695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length61
Median length46
Mean length26.417647
Min length5

Characters and Unicode

Total characters8982
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)100.0%

Sample

1st rowEgg & Cheese Muffin
2nd rowSausage McMuffm
3rd rowSausage & Egg McMuffm
4th rowVeg McMuffm
5th rowVeg Supreme Muffin
ValueCountFrequency (%)
large 62
 
4.6%
medium 58
 
4.3%
with 56
 
4.2%
chicken 54
 
4.0%
small 48
 
3.6%
biscuit 34
 
2.5%
egg 33
 
2.5%
iced 31
 
2.3%
latte 30
 
2.2%
nonfat 30
 
2.2%
Other values (193) 910
67.6%
2025-05-22T20:41:08.280257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1006
 
11.2%
e 851
 
9.5%
a 689
 
7.7%
i 509
 
5.7%
r 437
 
4.9%
t 381
 
4.2%
l 374
 
4.2%
c 363
 
4.0%
u 318
 
3.5%
h 308
 
3.4%
Other values (58) 3746
41.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6193
68.9%
Uppercase Letter 1321
 
14.7%
Space Separator 1006
 
11.2%
Close Punctuation 188
 
2.1%
Open Punctuation 188
 
2.1%
Other Punctuation 43
 
0.5%
Dash Punctuation 22
 
0.2%
Decimal Number 18
 
0.2%
Final Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 851
13.7%
a 689
11.1%
i 509
 
8.2%
r 437
 
7.1%
t 381
 
6.2%
l 374
 
6.0%
c 363
 
5.9%
u 318
 
5.1%
h 308
 
5.0%
o 284
 
4.6%
Other values (19) 1679
27.1%
Uppercase Letter
ValueCountFrequency (%)
C 223
16.9%
M 205
15.5%
S 199
15.1%
B 109
8.3%
L 95
7.2%
F 83
 
6.3%
P 50
 
3.8%
R 42
 
3.2%
N 40
 
3.0%
W 38
 
2.9%
Other values (13) 237
17.9%
Decimal Number
ValueCountFrequency (%)
0 6
33.3%
4 4
22.2%
1 3
16.7%
2 2
 
11.1%
6 2
 
11.1%
9 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
& 25
58.1%
, 11
25.6%
' 3
 
7.0%
% 2
 
4.7%
* 2
 
4.7%
Space Separator
ValueCountFrequency (%)
1006
100.0%
Close Punctuation
ValueCountFrequency (%)
) 188
100.0%
Open Punctuation
ValueCountFrequency (%)
( 188
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Final Punctuation
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7514
83.7%
Common 1468
 
16.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 851
 
11.3%
a 689
 
9.2%
i 509
 
6.8%
r 437
 
5.8%
t 381
 
5.1%
l 374
 
5.0%
c 363
 
4.8%
u 318
 
4.2%
h 308
 
4.1%
o 284
 
3.8%
Other values (42) 3000
39.9%
Common
ValueCountFrequency (%)
1006
68.5%
) 188
 
12.8%
( 188
 
12.8%
& 25
 
1.7%
- 22
 
1.5%
, 11
 
0.7%
0 6
 
0.4%
4 4
 
0.3%
3
 
0.2%
1 3
 
0.2%
Other values (6) 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8959
99.7%
None 10
 
0.1%
Alphabetic PF 10
 
0.1%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1006
 
11.2%
e 851
 
9.5%
a 689
 
7.7%
i 509
 
5.7%
r 437
 
4.9%
t 381
 
4.3%
l 374
 
4.2%
c 363
 
4.1%
u 318
 
3.5%
h 308
 
3.4%
Other values (53) 3723
41.6%
None
ValueCountFrequency (%)
é 9
90.0%
ñ 1
 
10.0%
Alphabetic PF
ValueCountFrequency (%)
8
80.0%
2
 
20.0%
Punctuation
ValueCountFrequency (%)
3
100.0%

Category
Categorical

High correlation 

Distinct14
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size23.3 KiB
Hot Beverages
99 
Breakfast
49 
Cold Beverages
45 
Smoothies & Shakes
34 
Chicken & Fish
26 
Other values (9)
87 

Length

Max length20
Median length18
Mean length12.711765
Min length6

Characters and Unicode

Total characters4322
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBreakfast
2nd rowBreakfast
3rd rowBreakfast
4th rowBreakfast
5th rowBreakfast

Common Values

ValueCountFrequency (%)
Hot Beverages 99
29.1%
Breakfast 49
14.4%
Cold Beverages 45
13.2%
Smoothies & Shakes 34
 
10.0%
Chicken & Fish 26
 
7.6%
Desserts 24
 
7.1%
Snacks & Sides 17
 
5.0%
Beef & Pork 15
 
4.4%
Sandwiches and Wraps 10
 
2.9%
Salads 6
 
1.8%
Other values (4) 15
 
4.4%

Length

2025-05-22T20:41:08.815225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beverages 144
20.7%
hot 99
14.2%
92
13.2%
breakfast 49
 
7.1%
cold 45
 
6.5%
smoothies 34
 
4.9%
shakes 34
 
4.9%
chicken 28
 
4.0%
fish 26
 
3.7%
desserts 24
 
3.5%
Other values (13) 120
17.3%

Most occurring characters

ValueCountFrequency (%)
e 695
16.1%
s 434
 
10.0%
355
 
8.2%
a 335
 
7.8%
r 247
 
5.7%
o 237
 
5.5%
t 219
 
5.1%
B 208
 
4.8%
g 152
 
3.5%
v 144
 
3.3%
Other values (21) 1296
30.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3282
75.9%
Uppercase Letter 593
 
13.7%
Space Separator 355
 
8.2%
Other Punctuation 92
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 695
21.2%
s 434
13.2%
a 335
10.2%
r 247
 
7.5%
o 237
 
7.2%
t 219
 
6.7%
g 152
 
4.6%
v 144
 
4.4%
k 143
 
4.4%
h 132
 
4.0%
Other values (10) 544
16.6%
Uppercase Letter
ValueCountFrequency (%)
B 208
35.1%
S 118
19.9%
H 99
16.7%
C 78
 
13.2%
F 26
 
4.4%
D 24
 
4.0%
P 20
 
3.4%
W 12
 
2.0%
N 8
 
1.3%
Space Separator
ValueCountFrequency (%)
355
100.0%
Other Punctuation
ValueCountFrequency (%)
& 92
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3875
89.7%
Common 447
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 695
17.9%
s 434
11.2%
a 335
 
8.6%
r 247
 
6.4%
o 237
 
6.1%
t 219
 
5.7%
B 208
 
5.4%
g 152
 
3.9%
v 144
 
3.7%
k 143
 
3.7%
Other values (19) 1061
27.4%
Common
ValueCountFrequency (%)
355
79.4%
& 92
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4322
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 695
16.1%
s 434
 
10.0%
355
 
8.2%
a 335
 
7.8%
r 247
 
5.7%
o 237
 
5.5%
t 219
 
5.1%
B 208
 
4.8%
g 152
 
3.5%
v 144
 
3.3%
Other values (21) 1296
30.0%

Serve_Size
Real number (ℝ)

High correlation 

Distinct146
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324.48235
Minimum9
Maximum907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-22T20:41:09.033233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile87
Q1163.75
median294
Q3453
95-th percentile623
Maximum907
Range898
Interquartile range (IQR)289.25

Descriptive statistics

Standard deviation195.48284
Coefficient of variation (CV)0.60244523
Kurtosis0.3065581
Mean324.48235
Median Absolute Deviation (MAD)151.5
Skewness0.80766343
Sum110324
Variance38213.542
MonotonicityNot monotonic
2025-05-22T20:41:09.215340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
453 45
 
13.2%
340 38
 
11.2%
623 20
 
5.9%
566 16
 
4.7%
200 9
 
2.6%
850 7
 
2.1%
595 7
 
2.1%
161 5
 
1.5%
907 5
 
1.5%
249 5
 
1.5%
Other values (136) 183
53.8%
ValueCountFrequency (%)
9 1
0.3%
10 1
0.3%
14 1
0.3%
25 2
0.6%
29 1
0.3%
33 2
0.6%
34 1
0.3%
38 1
0.3%
64 2
0.6%
65 1
0.3%
ValueCountFrequency (%)
907 5
 
1.5%
850 7
 
2.1%
646 1
 
0.3%
623 20
5.9%
595 7
 
2.1%
566 16
4.7%
500 1
 
0.3%
494 4
 
1.2%
479 1
 
0.3%
460 1
 
0.3%

Energy
Real number (ℝ)

High correlation  Zeros 

Distinct151
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean337.8
Minimum0
Maximum1880
Zeros18
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:09.377621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1176.5
median299.5
Q3460
95-th percentile740.5
Maximum1880
Range1880
Interquartile range (IQR)283.5

Descriptive statistics

Standard deviation231.8508
Coefficient of variation (CV)0.68635523
Kurtosis5.702213
Mean337.8
Median Absolute Deviation (MAD)139.5
Skewness1.4792953
Sum114852
Variance53754.792
MonotonicityNot monotonic
2025-05-22T20:41:09.577888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
5.3%
340 10
 
2.9%
430 10
 
2.9%
280 9
 
2.6%
250 8
 
2.4%
330 7
 
2.1%
140 7
 
2.1%
260 7
 
2.1%
270 6
 
1.8%
100 6
 
1.8%
Other values (141) 252
74.1%
ValueCountFrequency (%)
0 18
5.3%
1 1
 
0.3%
7 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
15 1
 
0.3%
20 1
 
0.3%
26 1
 
0.3%
34 1
 
0.3%
45 1
 
0.3%
ValueCountFrequency (%)
1880 1
0.3%
1150 1
0.3%
1090 1
0.3%
1050 1
0.3%
990 1
0.3%
940 1
0.3%
930 1
0.3%
850 2
0.6%
820 2
0.6%
810 1
0.3%

Protein
Real number (ℝ)

High correlation  Zeros 

Distinct42
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.694118
Minimum0
Maximum87
Zeros50
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:09.774932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median10
Q317
95-th percentile32.05
Maximum87
Range87
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.99077
Coefficient of variation (CV)0.93985454
Kurtosis6.0816539
Mean11.694118
Median Absolute Deviation (MAD)8
Skewness1.6390433
Sum3976
Variance120.79702
MonotonicityNot monotonic
2025-05-22T20:41:09.936068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 50
 
14.7%
2 21
 
6.2%
1 19
 
5.6%
12 16
 
4.7%
9 16
 
4.7%
10 15
 
4.4%
15 14
 
4.1%
11 14
 
4.1%
4 13
 
3.8%
14 13
 
3.8%
Other values (32) 149
43.8%
ValueCountFrequency (%)
0 50
14.7%
1 19
 
5.6%
2 21
6.2%
3 10
 
2.9%
4 13
 
3.8%
5 10
 
2.9%
6 6
 
1.8%
7 6
 
1.8%
8 11
 
3.2%
9 16
 
4.7%
ValueCountFrequency (%)
87 1
 
0.3%
48 1
 
0.3%
44 1
 
0.3%
40 2
 
0.6%
39 1
 
0.3%
37 2
 
0.6%
36 6
1.8%
35 2
 
0.6%
33 1
 
0.3%
32 2
 
0.6%

Total_Fat
Real number (ℝ)

High correlation  Zeros 

Distinct55
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.05
Minimum0
Maximum118
Zeros71
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:10.121166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.375
median10
Q320
95-th percentile35
Maximum118
Range118
Interquartile range (IQR)18.625

Descriptive statistics

Standard deviation13.639604
Coefficient of variation (CV)1.0451804
Kurtosis10.300397
Mean13.05
Median Absolute Deviation (MAD)9.5
Skewness2.1278194
Sum4437
Variance186.03879
MonotonicityNot monotonic
2025-05-22T20:41:10.335625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
20.9%
9 15
 
4.4%
16 12
 
3.5%
23 12
 
3.5%
8 11
 
3.2%
20 11
 
3.2%
11 9
 
2.6%
2 9
 
2.6%
4 9
 
2.6%
13 9
 
2.6%
Other values (45) 172
50.6%
ValueCountFrequency (%)
0 71
20.9%
0.5 8
 
2.4%
1 6
 
1.8%
1.5 1
 
0.3%
2 9
 
2.6%
2.5 1
 
0.3%
3 1
 
0.3%
3.5 8
 
2.4%
4 9
 
2.6%
4.5 6
 
1.8%
ValueCountFrequency (%)
118 1
0.3%
60 1
0.3%
59 1
0.3%
56 1
0.3%
52 1
0.3%
51 1
0.3%
50 1
0.3%
48 1
0.3%
46 2
0.6%
43 1
0.3%

Saturated_Fat
Real number (ℝ)

High correlation  Zeros 

Distinct64
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5955882
Minimum0
Maximum24.1
Zeros83
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:10.559053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median4.55
Q39
95-th percentile15.05
Maximum24.1
Range24.1
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation5.2194224
Coefficient of variation (CV)0.93277456
Kurtosis0.10323365
Mean5.5955882
Median Absolute Deviation (MAD)4.2
Skewness0.85725856
Sum1902.5
Variance27.24237
MonotonicityNot monotonic
2025-05-22T20:41:10.768362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 83
24.4%
8 18
 
5.3%
10 16
 
4.7%
6 16
 
4.7%
7 14
 
4.1%
3 14
 
4.1%
12 12
 
3.5%
5 11
 
3.2%
4.5 11
 
3.2%
3.5 11
 
3.2%
Other values (54) 134
39.4%
ValueCountFrequency (%)
0 83
24.4%
0.5 3
 
0.9%
0.9 1
 
0.3%
1 3
 
0.9%
1.1 5
 
1.5%
1.2 1
 
0.3%
1.4 2
 
0.6%
1.5 5
 
1.5%
2 10
 
2.9%
2.1 1
 
0.3%
ValueCountFrequency (%)
24.1 1
 
0.3%
20 4
1.2%
19 2
 
0.6%
18 1
 
0.3%
17.9 1
 
0.3%
17 4
1.2%
16.2 1
 
0.3%
16 3
 
0.9%
15 8
2.4%
14.6 1
 
0.3%

Trans_Fat
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18323529
Minimum0
Maximum2.5
Zeros229
Zeros (%)67.4%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:10.891486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.1
95-th percentile1
Maximum2.5
Range2.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.38171497
Coefficient of variation (CV)2.0831956
Kurtosis6.4083938
Mean0.18323529
Median Absolute Deviation (MAD)0
Skewness2.4727712
Sum62.3
Variance0.14570632
MonotonicityNot monotonic
2025-05-22T20:41:11.036548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 229
67.4%
0.1 32
 
9.4%
1 30
 
8.8%
0.5 18
 
5.3%
0.2 17
 
5.0%
1.5 8
 
2.4%
0.6 2
 
0.6%
0.3 2
 
0.6%
0.4 1
 
0.3%
2.5 1
 
0.3%
ValueCountFrequency (%)
0 229
67.4%
0.1 32
 
9.4%
0.2 17
 
5.0%
0.3 2
 
0.6%
0.4 1
 
0.3%
0.5 18
 
5.3%
0.6 2
 
0.6%
1 30
 
8.8%
1.5 8
 
2.4%
2.5 1
 
0.3%
ValueCountFrequency (%)
2.5 1
 
0.3%
1.5 8
 
2.4%
1 30
 
8.8%
0.6 2
 
0.6%
0.5 18
 
5.3%
0.4 1
 
0.3%
0.3 2
 
0.6%
0.2 17
 
5.0%
0.1 32
 
9.4%
0 229
67.4%

Cholestrol
Real number (ℝ)

High correlation  Zeros 

Distinct59
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.544118
Minimum0
Maximum575
Zeros70
Zeros (%)20.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:11.220472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median25
Q355
95-th percentile250
Maximum575
Range575
Interquartile range (IQR)51

Descriptive statistics

Standard deviation81.815469
Coefficient of variation (CV)1.7208326
Kurtosis18.847191
Mean47.544118
Median Absolute Deviation (MAD)24.5
Skewness3.8984033
Sum16165
Variance6693.7709
MonotonicityNot monotonic
2025-05-22T20:41:11.405785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
20.6%
5 26
 
7.6%
35 20
 
5.9%
25 17
 
5.0%
40 14
 
4.1%
30 14
 
4.1%
50 14
 
4.1%
15 12
 
3.5%
10 11
 
3.2%
20 9
 
2.6%
Other values (49) 133
39.1%
ValueCountFrequency (%)
0 70
20.6%
1 4
 
1.2%
3 8
 
2.4%
4 4
 
1.2%
5 26
 
7.6%
6 4
 
1.2%
7 2
 
0.6%
8 1
 
0.3%
9 3
 
0.9%
10 11
 
3.2%
ValueCountFrequency (%)
575 2
0.6%
555 2
0.6%
300 1
0.3%
295 1
0.3%
285 1
0.3%
280 1
0.3%
277 1
0.3%
275 1
0.3%
265 2
0.6%
260 1
0.3%

Carbohydrates
Real number (ℝ)

High correlation  Zeros 

Distinct92
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.770588
Minimum0
Maximum141
Zeros19
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:11.620697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128
median41
Q356
95-th percentile98.4
Maximum141
Range141
Interquartile range (IQR)28

Descriptive statistics

Standard deviation26.9364
Coefficient of variation (CV)0.61539954
Kurtosis1.8172282
Mean43.770588
Median Absolute Deviation (MAD)14
Skewness0.99750313
Sum14882
Variance725.56963
MonotonicityNot monotonic
2025-05-22T20:41:11.769556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
5.6%
30 11
 
3.2%
50 10
 
2.9%
34 9
 
2.6%
38 9
 
2.6%
41 9
 
2.6%
42 9
 
2.6%
49 8
 
2.4%
60 7
 
2.1%
32 7
 
2.1%
Other values (82) 242
71.2%
ValueCountFrequency (%)
0 19
5.6%
1 2
 
0.6%
2 3
 
0.9%
3 1
 
0.3%
4 2
 
0.6%
7 1
 
0.3%
8 2
 
0.6%
9 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
ValueCountFrequency (%)
141 1
 
0.3%
140 1
 
0.3%
139 1
 
0.3%
135 2
0.6%
118 1
 
0.3%
116 1
 
0.3%
115 1
 
0.3%
114 3
0.9%
111 2
0.6%
110 1
 
0.3%

Sugars
Real number (ℝ)

High correlation  Zeros 

Distinct88
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.347059
Minimum0
Maximum128
Zeros36
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:11.952064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median16
Q343.25
95-th percentile77.1
Maximum128
Range128
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation26.89634
Coefficient of variation (CV)1.0208479
Kurtosis1.0819862
Mean26.347059
Median Absolute Deviation (MAD)14
Skewness1.1916445
Sum8958
Variance723.41312
MonotonicityNot monotonic
2025-05-22T20:41:12.188128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
10.6%
2 23
 
6.8%
3 19
 
5.6%
7 12
 
3.5%
6 11
 
3.2%
15 8
 
2.4%
5 8
 
2.4%
45 7
 
2.1%
59 7
 
2.1%
8 7
 
2.1%
Other values (78) 202
59.4%
ValueCountFrequency (%)
0 36
10.6%
1 5
 
1.5%
2 23
6.8%
3 19
5.6%
4 7
 
2.1%
5 8
 
2.4%
6 11
 
3.2%
7 12
 
3.5%
8 7
 
2.1%
9 4
 
1.2%
ValueCountFrequency (%)
128 1
0.3%
123 1
0.3%
120 1
0.3%
115 1
0.3%
103 1
0.3%
101 1
0.3%
100 1
0.3%
99 1
0.3%
97 1
0.3%
93 1
0.3%

Dietary_Fibre
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5441176
Minimum0
Maximum9
Zeros117
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-05-22T20:41:12.393997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6637185
Coefficient of variation (CV)1.0774558
Kurtosis1.7571833
Mean1.5441176
Median Absolute Deviation (MAD)1
Skewness1.2929103
Sum525
Variance2.7679594
MonotonicityNot monotonic
2025-05-22T20:41:12.533735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 117
34.4%
1 85
25.0%
2 52
15.3%
3 47
13.8%
4 18
 
5.3%
5 11
 
3.2%
7 5
 
1.5%
6 4
 
1.2%
9 1
 
0.3%
ValueCountFrequency (%)
0 117
34.4%
1 85
25.0%
2 52
15.3%
3 47
13.8%
4 18
 
5.3%
5 11
 
3.2%
6 4
 
1.2%
7 5
 
1.5%
9 1
 
0.3%
ValueCountFrequency (%)
9 1
 
0.3%
7 5
 
1.5%
6 4
 
1.2%
5 11
 
3.2%
4 18
 
5.3%
3 47
13.8%
2 52
15.3%
1 85
25.0%
0 117
34.4%

Sodium
Real number (ℝ)

High correlation  Zeros 

Distinct134
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean490.45588
Minimum0
Maximum3600
Zeros14
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2025-05-22T20:41:12.732342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q1118.75
median230
Q3790
95-th percentile1480
Maximum3600
Range3600
Interquartile range (IQR)671.25

Descriptive statistics

Standard deviation538.22774
Coefficient of variation (CV)1.097403
Kurtosis3.3505596
Mean490.45588
Median Absolute Deviation (MAD)182.5
Skewness1.6021079
Sum166755
Variance289689.1
MonotonicityNot monotonic
2025-05-22T20:41:12.956091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 16
 
4.7%
0 14
 
4.1%
180 11
 
3.2%
150 8
 
2.4%
190 8
 
2.4%
170 8
 
2.4%
140 8
 
2.4%
135 7
 
2.1%
10 7
 
2.1%
50 6
 
1.8%
Other values (124) 247
72.6%
ValueCountFrequency (%)
0 14
4.1%
5 5
 
1.5%
10 7
2.1%
15 3
 
0.9%
20 4
 
1.2%
25 1
 
0.3%
30 2
 
0.6%
35 4
 
1.2%
40 3
 
0.9%
45 3
 
0.9%
ValueCountFrequency (%)
3600 1
0.3%
2290 1
0.3%
2260 1
0.3%
2170 1
0.3%
2150 1
0.3%
2030 1
0.3%
1830 1
0.3%
1800 1
0.3%
1720 1
0.3%
1700 1
0.3%

Interactions

2025-05-22T20:40:55.538057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:08.210672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:10.779474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:13.738595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:16.596632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:19.376874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:21.944140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:24.694899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:27.932990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:30.911154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:33.851313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:36.522703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:39.288231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:41.859112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:44.930587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:47.585712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:50.476956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:53.214997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:55.649420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:08.439259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:10.957926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:14.160770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:16.726778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:19.520265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:22.116980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:24.826922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:28.108647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:31.105095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:33.960079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:36.683928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:39.488290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:42.312809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:45.092684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:47.800136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:50.652978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:53.333434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:55.765246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:08.591697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:11.073045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:14.311742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:16.873667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:19.644674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:22.287487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:24.985678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:28.262890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:31.272336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:34.160248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:36.860504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:39.652583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:42.465622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:45.183006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:47.959946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:50.820665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:53.502551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:56.201068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:08.756422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:11.209380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:14.425879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:17.015132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:19.781794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:22.428245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:25.135104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:28.417257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:31.406745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:34.322258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:37.024308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:39.799864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:42.577745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:45.320505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:48.126660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:50.935105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:53.617891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:56.400831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:08.874594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:11.364850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:14.557935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:17.166289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:19.941226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:22.621377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:25.280870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:28.588807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:31.608612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:34.435967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:37.198971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:39.956989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:42.719202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:45.420875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:48.264423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:51.093202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:53.799050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:56.512457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:09.009405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:11.514426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:14.739003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:17.336583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:20.074322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:22.775327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:25.396658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:28.743539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:31.775149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:34.567285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:37.349995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:40.064084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:42.898253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:45.570723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:48.428158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:51.245145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:53.909670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:56.632000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:09.126586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:11.633954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:14.904145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:17.492460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:20.195294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:22.916199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:25.561570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:28.897450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:31.977924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:34.726506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:37.530932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:40.238171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:43.061333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:45.687468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:48.592118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:51.423975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:54.055180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:56.805106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:09.259572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:11.788851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:14.986142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:17.641782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:20.345683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:23.109021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:25.782019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:29.111648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:32.138020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:34.893227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:37.671027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:40.380605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:43.228495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:45.862409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:48.759309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:51.580912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:54.172338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:56.938949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:09.426108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:11.948523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:15.153544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:17.801508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:20.474709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:23.232619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:25.893645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:29.247968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:32.261597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:43.395592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:46.007576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:48.913159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:51.719943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:54.271375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:57.104430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:09.540485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:12.096869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:15.277697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:17.959313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:20.583775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:23.381899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:26.084533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:29.445848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:32.434303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:35.207601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:37.934296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:40.593449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:43.563526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:46.183860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:49.063888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:51.854069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:40.742292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:43.692935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:49.224874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:51.950293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:57.434817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:09.815916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:18.270158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:23.694604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:26.510756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:29.786582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:32.719510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:35.530040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:09.925496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:15.712427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:18.399381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:21.019747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:23.869836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:26.743731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:29.941116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:32.880378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:35.662057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:38.334979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:40.980064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:44.036063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:46.634029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:49.524662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:52.276666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:54.754667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:57.701017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:10.042104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:12.736908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:15.883311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:18.557341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:21.169460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:24.051790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:26.882892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:30.083890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:33.017359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:35.784581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:38.474032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:41.106841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:44.205636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:46.787092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:49.689233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:52.426102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:54.924545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:57.846411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:10.197780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:12.910974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:16.040550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:18.703485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:21.331746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:24.171824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:27.042949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:30.239223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:33.140392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:35.898516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:38.609070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:41.252018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:44.381121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:46.924057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:49.864440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:52.559354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:55.097645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:57.982980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:10.359665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:13.109438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:16.202316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:18.886740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:21.511300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:24.298661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:27.190934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:30.430348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:33.327826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:36.050354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:38.782227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:41.402056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:44.521488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:47.067033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:50.021541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:52.685545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:55.206832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:58.128669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:10.485109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:13.275547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:16.363349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:19.041829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:21.684361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:24.463586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:27.639223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:33.506980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:41.582916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:50.163444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:52.887730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:58.240558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2025-05-22T20:40:19.168660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:21.818388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:24.578673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:27.742818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:30.732143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:33.691420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:36.353624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:39.069372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:41.705235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:44.818679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:47.439198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:50.311428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:53.074397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-22T20:40:55.396627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-05-22T20:41:13.179670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CarbohydratesCategoryCholestrolCountryCustomersDietary_FibreEnergyGross_Profit_MarginLatitudeLongitudeNumber_of_EmployeesOwnership_TypeProfitsProteinRevenueSaturated_FatServe_SizeSodiumStateSugarsTotal_FatTrans_Fat
Carbohydrates1.0000.2230.3940.229-0.0590.3450.7660.1130.254-0.1330.1270.1400.1210.4210.1920.4710.3800.2480.3390.6600.4370.304
Category0.2231.0000.2920.6420.0620.3810.2700.1160.4460.4420.1250.4440.1270.3720.1970.2520.3730.4110.7020.2730.2730.300
Cholestrol0.3940.2921.0000.194-0.0420.5350.8020.0690.415-0.2830.0990.1310.1890.8520.2060.829-0.0770.6660.3510.0100.8430.420
Country0.2290.6420.1941.0000.0000.3200.2650.6600.8390.9960.6060.9990.7590.3050.9880.1760.4730.4310.9260.2120.0750.214
Customers-0.0590.062-0.0420.0001.000-0.144-0.030-0.8290.0410.0000.2200.0000.613-0.0970.1120.0100.043-0.0610.060-0.023-0.013-0.018
Dietary_Fibre0.3450.3810.5350.320-0.1441.0000.6560.1670.242-0.2020.0690.2280.0440.7250.1300.513-0.2870.6780.410-0.2560.6710.156
Energy0.7660.2700.8020.265-0.0300.6561.0000.0670.334-0.1950.0880.1760.1430.8170.1680.8540.0080.6880.3940.1950.8900.468
Gross_Profit_Margin0.1130.1160.0690.660-0.8290.1670.0671.0000.208-0.2690.1540.468-0.2580.1380.259-0.0280.111-0.0220.2190.0940.006-0.124
Latitude0.2540.4460.4150.8390.0410.2420.3340.2081.000-0.5220.3920.6010.5220.3180.5570.3060.2630.0520.8540.1160.318-0.106
Longitude-0.1330.442-0.2830.9960.000-0.202-0.195-0.269-0.5221.000-0.3980.706-0.508-0.285-0.569-0.125-0.1570.0290.934-0.145-0.1410.234
Number_of_Employees0.1270.1250.0990.6060.2200.0690.0880.1540.392-0.3981.0000.4220.7440.0940.933-0.0010.226-0.1010.1770.1570.017-0.182
Ownership_Type0.1400.4440.1310.9990.0000.2280.1760.4680.6010.7060.4221.0000.5410.2050.6970.1280.3370.3060.6780.1400.0870.146
Profits0.1210.1270.1890.7590.6130.0440.143-0.2580.522-0.5080.7440.5411.0000.1340.8060.0960.277-0.1240.2530.1210.093-0.209
Protein0.4210.3720.8520.305-0.0970.7250.8170.1380.318-0.2850.0940.2050.1341.0000.1940.698-0.1330.7860.472-0.0760.7930.321
Revenue0.1920.1970.2060.9880.1120.1300.1680.2590.557-0.5690.9330.6970.8060.1941.0000.0600.310-0.1140.3170.1890.079-0.230
Saturated_Fat0.4710.2520.8290.1760.0100.5130.854-0.0280.306-0.125-0.0010.1280.0960.6980.0601.000-0.1260.6450.3190.0170.9380.558
Serve_Size0.3800.373-0.0770.4730.043-0.2870.0080.1110.263-0.1570.2260.3370.277-0.1330.310-0.1261.000-0.4510.3820.574-0.252-0.124
Sodium0.2480.4110.6660.431-0.0610.6780.688-0.0220.0520.029-0.1010.306-0.1240.786-0.1140.645-0.4511.0000.456-0.3050.7720.361
State0.3390.7020.3510.9260.0600.4100.3940.2190.8540.9340.1770.6780.2530.4720.3170.3190.3820.4561.0000.3720.3150.371
Sugars0.6600.2730.0100.212-0.023-0.2560.1950.0940.116-0.1450.1570.1400.121-0.0760.1890.0170.574-0.3050.3721.000-0.1420.109
Total_Fat0.4370.2730.8430.075-0.0130.6710.8900.0060.318-0.1410.0170.0870.0930.7930.0790.938-0.2520.7720.315-0.1421.0000.483
Trans_Fat0.3040.3000.4200.214-0.0180.1560.468-0.124-0.1060.234-0.1820.146-0.2090.321-0.2300.558-0.1240.3610.3710.1090.4831.000

Missing values

2025-05-22T20:40:58.516061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-22T20:40:59.096338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Store_IDStore_NameOwnership_TypeStreet_AddressCityStateCountryLongitudeLatitudeRevenueProfitsGross_Profit_MarginNumber_of_EmployeesCustomersBest_Selling_ItemCategoryServe_SizeEnergyProteinTotal_FatSaturated_FatTrans_FatCholestrolCarbohydratesSugarsDietary_FibreSodium
023149-228271Banjara HillsJoint VentureLower Ground Floor, GVK One, Road Number 1, Banjara HillsHyderabadAPIN78.4517.422.1173440.1715840.747732343979Egg & Cheese MuffinBreakfast1122901413.07.00.22442822620
123191-228548KukatpallyJoint VentureUpper Ground Floor, Forum Sujana Mall, KukatpallyHyderabadAPIN78.3917.481.0585040.0546450.442299251156Sausage McMuffmBreakfast1122731611.05.70.2502822950
223193-228546MadhapurJoint VentureLower Ground Floor, Inorbit Mall, MadhapurHyderabadAPIN78.3917.434.5050200.6638670.9335885410346Sausage & Egg McMuffmBreakfast1573552217.07.90.227729221020
323180-228545Jubilee HillsJoint VentureGround Floor, Road No. 92, Near Apollo hospital, Jubilee HillsHyderabadAPIN78.4217.423.4055300.3978350.934559457414Veg McMuffmBreakfast1192991012.07.20.22138331000
424457-238129Hi-Tech CityJoint VentureUpper Ground Floor, Phoenix tower A, Opposite Trident Hotel, Madhapur VillageHyderabadAPIN78.3817.457.3327131.6565240.2524617717887Veg Supreme MuffinBreakfast139299713.05.20.2113944960
523664-232349MindspaceJoint VentureMindspace, Hitech CityHyderabadAPIN78.3817.453.1196020.3396560.910604426652Hot Cakes with Maple SyrupBreakfast142372610.06.40.4166422630
620874-208485Punjabi BaghJoint Venture1/83, Ground Floor , Club Road, West Punjabi BaghDelhiDLIN77.1328.675.1952570.8651920.8574865912187Hash BrownBreakfast64166210.04.80.111622370
729491-254065T3 Domestic ArrivalsJoint VentureIGI AirportDelhiDLIN77.1228.577.7315291.8322710.0777598118950McEgg BurgerBreakfast1152811212.03.80.12273222700
822446-221249HUDA Metro StationJoint VentureGround Floor, South Wing, HUDA Metro StationGurgaonDLIN77.0728.462.1140480.1711230.746994343970Chicken Maharaja MacSandwiches and Wraps2465452626.08.90.26452751030
923392-229965R K PuramJoint VentureSangam Courtyard, Major Somnath Marg, Sector 9, R.K PuramNew DelhiDLIN77.1728.574.6541640.7051270.9220765510744McChickenSandwiches and Wraps1734311520.06.00.2314953840
Store_IDStore_NameOwnership_TypeStreet_AddressCityStateCountryLongitudeLatitudeRevenueProfitsGross_Profit_MarginNumber_of_EmployeesCustomersBest_Selling_ItemCategoryServe_SizeEnergyProteinTotal_FatSaturated_FatTrans_FatCholestrolCarbohydratesSugarsDietary_FibreSodium
3308650-20641BillericaCompany Owned199 Boston Road, Unit 15BillericaMAUS-71.2942.5820.3065581.6613927.136423654054Shamrock Shake (Medium)Smoothies & Shakes4536601419.012.01.075109930210
33122253-2188261304 Commonwealth Ave.Company Owned1304 Commonwealth AvenueBostonMAUS-71.1342.3515.7712033.6396210.3741015218354Shamrock Shake (Large)Smoothies & Shakes6238201823.015.01.0901351150260
3327544-27387755 Boylston StCompany Owned755 Boylston Street, TrilogyBostonMAUS-71.0842.3534.1048926.4740703.88136110414423McFlurry with M&M’s Candies (Small)Smoothies & Shakes3106501323.014.00.55096891180
33311726-105182Fenway Triangle TrilogyCompany Owned142-148 Brookline AvenueBostonMAUS-71.1042.3439.4189289.913800-0.86201511920343McFlurry with M&M’s Candies (Medium)Smoothies & Shakes4609302033.020.01.0751391282260
33425967-242726Smiths Fort Mohave #473Licensed4747 Highway 95Fort MohaveAZUS-114.6035.0221.5509513.3443214.0952866811097McFlurry with M&M’s Candies (Snack)Smoothies & Shakes207430915.010.00.03564591120
33574975-101172Safeway - Fort Mohave #1474Licensed4823 S Hwy 95Fort MohaveAZUS-114.6035.0216.3853881.4354655.549639534610McFlurry with Oreo Cookies (Small)Smoothies & Shakes2855101217.09.00.54580641280
3365586-1312E. Palisades & Ave of the FountainCompany Owned16425 E. Palisades Blvd., Suite 101Fountain HillsAZUS-111.7333.6134.3324088.797738-1.10980210520800McFlurry with Oreo Cookies (Medium)Smoothies & Shakes3816901523.012.01.055106851380
33774539-65039Safeway - Fountain Hills #1291Licensed13733 Fountain Hills BlvdFountain HillsAZUS-111.7333.6122.2078626.406169-2.2916797023892McFlurry with Oreo Cookies (Snack)Smoothies & Shakes190340811.06.00.03053431190
33820344-204610Target Fountain Hills T-1432Licensed16825 E. Shea BoulevardFountain HillsAZUS-111.7233.5748.7449267.05333910.38710114610091McFlurry with Reese's Peanut Butter Cups (Medium)Smoothies & Shakes4038102132.015.01.0601141032400
33976302-96624Super Target Gilbert ST-1960Licensed3931 S Gilbert RdGilbertAZUS-111.7933.2820.2285842.8438674.493540649696McFlurry with Reese's Peanut Butter Cups (Snack)Smoothies & Shakes2024101016.08.00.03057511200